Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Making

• URL: http://arxiv.org/abs/2601.08247v1
• Date: Tue, 13 Jan 2026 06:09:24 GMT
• Title: Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Making
• Authors: Liu He,
• Abstract summary: This study integrates cognitive biases into reinforcement learning frameworks for financial trading.<n>We introduce biases, such as overconfidence and loss aversion, into reward structures and decision-making processes.<n>Despite its inconclusive or negative results, this study provides insights into the challenges of incorporating human-like biases into RL.
• Score: 6.60395970974896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Financial markets are influenced by human behavior that deviates from rationality due to cognitive biases. Traditional reinforcement learning (RL) models for financial decision-making assume rational agents, potentially overlooking the impact of psychological factors. This study integrates cognitive biases into RL frameworks for financial trading, hypothesizing that such models can exhibit human-like trading behavior and achieve better risk-adjusted returns than standard RL agents. We introduce biases, such as overconfidence and loss aversion, into reward structures and decision-making processes and evaluate their performance in simulated and real-world trading environments. Despite its inconclusive or negative results, this study provides insights into the challenges of incorporating human-like biases into RL, offering valuable lessons for developing robust financial AI systems.

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